Joint symbol, amplitude, and rate estimator

ABSTRACT

The system in one embodiment relates to tightly integrating parameter estimation, symbol hypothesis testing, decoding, and rate identification. The present invention provides Turbo-decoding for joint signal demodulation based on an iterative decoding solution that exploits error correction codes. The system iteratively couples an initial amplitude estimator, a symbol estimator, a bank of decoders, and a joint amplitude estimator to produce the symbol estimates.

RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.10/531,772, filed Apr. 19, 2005, which is a U.S. National PhaseApplication of PCT Application No. PCT/US2003/038190, filed Nov. 25,2003, which claims the benefit of U.S. Provisional Application No.60/462657, filed Apr. 14, 2003. This application is also related to U.S.patent application Ser. No. 10/105,918, filed Mar. 25, 2002 entitled“System for Decreasing Processing Time in an Iterative Multi-UserDetector System”; U.S. patent application Ser. No. 10/414,738 filed Apr.16, 2003 entitled “System and Method for Increasing Throughput in aMultiuser Detection Based Multiple Access Communications System”; U.S.patent application Ser. No. 10/208,409 entitled “Power and ConfidenceOrdered Low Complexity Soft TurboMUD with Voting System”; and U.S.Patent Application Serial No. not yet assigned, filed Oct. 17, 2008entitled “JOINT SYMBOL, AMPLITURE, AND RATE ESTIMATOR”. Each of theseapplications is herein incorporated in their entirety by reference.

FIELD OF THE INVENTION

The present invention relates to communications and more particularly toco-channel communications, and more particularly to increasing capacityby allowing interfering channels.

BACKGROUND OF THE INVENTION

The telecommunications industry has been expanding at an unprecedentedgrowth rate throughout the World. In particular, the wireless sector,including cell phones, PCS, wireless local area networks and Bluetoothsuch as IS95, GSM, 3G, IEEE 802.11a/b/g, and 802.16 has grown far beyondexpectations and at a much higher rate than the fixed telecommunications(wired) counterpart. The ability to access data and communicate anywhereat anytime has enormous potential and commercial value.

The content of the wireless sector is also changing, with more and moredata being transmitted, including Internet connectivity and live feeds.The usage involving personal digital assistants (PDA's) and even smartappliances have created new markets utilizing wireless datacommunications. Despite advancements in wireless transmission andreception, there is a growing problem of extracting more informationsignals within a limited bandwidth.

Emerging multiple-access receiver processing procedures allow formultiple users to access the same communications medium to transmit orreceive information. Multiple access communication systems allow thetransmission of multiple digital data streams between multipletransmitting and receiving devices. However, since many users transmitenergy on the same communications channel, a number of inherentdifficulties arise, particularly when receivers attempt to detect theinformation associated with a particular user when there is heavy signalinterference created by other users of the system at the same time.Typically the signal of interest cannot be received or the quality ofreception is significantly degraded.

For example, a base station that processes a number of cellular deviceshas to receive and transmit data within a certain frequency range. Theability to extract the correct data from a given user is a difficulttask when the effects of interference and multipaths are considered. Theproblem is further complicated when the number of users exceeds thenumber of dimensions (e.g. time slots, frequency slots, polarizations,etc), resulting in an overloaded condition.

In addition to the problems associated with multiple users in a givenbandwidth, an additional problem is the inability to process the data inthe receivers in real time. Advanced receiver techniques cover severalareas, namely interference suppression (also called Multi-User Detectionor MUD), multipath combining and space-time processing, equalization,and channel estimation. These various techniques can be mixed andmatched depending upon the circumstances.

One way of alleviating some of the multiple access problems is toseparate the interfering transmissions at the receiver using signalprocessing techniques. However, state of the art receivers are notcapable of detecting and decoding the information associated with eachuser under conditions of heavy interference. Another solution to theco-channel interference problem is to decrease the number of users perchannel. This, of course, is not an attractive option fortelecommunication companies, since obtaining the maximum number of usersor managing peak volume transmission periods are important businessobjectives.

It should be understood that the discussion herein illustrates wirelesscellular communications the multiple access topologies are equallyapplicable to wired cable systems and local area networks, read/writeoperations of a disc drive, satellite communications and any applicationthat benefits from extracting digital information from among manymultiple interfering signals.

Several techniques are used to improve results in co-channel multipleaccess communications systems. Frequency-Division Multiple Access (FDMA)assigns a different frequency to each user and parses an allocated bandfor a communication system wherein a single user's signal transmissionpower is concentrated into a single narrower radio frequency band.Interference from adjacent channels is limited by the use of band passfilters, however for each channel being assigned a different frequencysystem the total capacity is limited by the available frequency slotsand by physical limitations imposed by frequency reuse. In a cellulartelephone configuration this poses problems because all proximate cellsmust operate on different frequencies. However, frequency bands may bere-used, provided that the same frequency cells are positioned at acertain distance apart. A further drawback with FDMA schemes is thatusers will pay full-time for their assigned frequency regardless oftheir actual use of the system.

Code Division Multiple Access (CDMA) is another multiplexing techniquewherein for each communication channel the signals are encoded using asequence known to the transmitter and the receiver for that specificchannel. In CDMA, all users use the same frequency at the same time.However, before transmission, the signal from each user is multiplied bya distinct signature waveform. The signature waveform is a signal thathas a larger bandwidth than the information-bearing signal from theuser. However, in a CDMA system, the total level of co-channelinterference limits the number of active users at any instant of time.

In Time Division Multiple Access (TDMA) technology, multiple channels ofdata are temporally interleaved, i.e. each signal is assigned to adifferent time interval and the signals are transmitted individually,according to their assigned time slot. The TDMA channel consists of atime slot or frame in a periodic train of time intervals over the samefrequency, with a given signal's energy confined to one of these timeslots. However, in a TDMA system, all transmitters and receivers musthave access to a common clock, as time-synchronization among the usersis required. Adjacent channel interference is limited by the use of atime gate or other synchronization element that only passes signalenergy received at the proper time. The system capacity is limited bythe available time slots (within a given frequency band) as well as byphysical limitations imposed by frequency reuse, as each channel isassigned a different time slot within a particular frequency band.

One of the goals of FDMA and TDMA systems is to try and prevent twopotentially interfering signals from occupying the same frequency at thesame time. In contrast, Code Division Multiple Access (CDMA) techniquesallow signals to overlap in both time and frequency. CDMA signals sharethe same frequency spectrum at the same time, hence, the CDMA signalsappear to overlap one another. The scrambled signal format of CDMAvirtually eliminates cross talk between interfering transmitters.

In a CDMA system, each signal is transmitted using spread spectrumtechniques. The transmitted informational data stream is impressed upona much higher rate data stream termed a signature sequence. The bitstream of the signature sequence data is typically binary, and can begenerated using a pseudo-noise (PN) process that appears random, but canbe replicated by an authorized receiver. The informational data streamand the high bit rate signature sequence stream are combined bymultiplying the two bit streams together, assuming the binary values ofthe two bit streams are represented by +1 or −1. This combination of thehigher bit rate signal with the lower bit rate data stream is calledspreading the informational data stream signal. Each informational datastream or channel is allocated a unique signature sequence.

In operation, a stream of spread information signature signals aremodulated by weights corresponding to the information that is to betransmitted. Some modulation examples include binary phase shift keying(BPSK) and quadrature phase shift keying (QPSK). If several transmittersmodulate their data onto the signature waveform and modulate again withthe carrier tone, a radio frequency (RF) signal comprised of acontinuous stream of information modulated signature pulses will bepresent at the receiver, one corresponding to each transmitter. Theplurality of transmitted signals and are jointly received as a compositesignal at the receiver. Each of the spread signals overlaps all of theother spread signals in time and frequency. Moreover, environmentalnoise as well as receiver electronic noise is also present in themeasured received signal. The state of the art receiver correlates thecomposite noisy signal with one of the unique signature sequences, andthe corresponding information signal is isolated and despread while theother signals appear as only small additions to the noise floor.

A signature sequence is often used to represent one bit of information.Receiving the transmitted sequence or its complement indicates whetherthe information bit is a +1 or −1, sometimes denoted “0” or “1”. Thesignature sequence usually comprises N pulses, and each pulse is calleda “chip”. The entire N-chip sequence, or its complement, depending onthe information bit to be conveyed, is referred to as a transmittedsymbol.

The receiver correlates the received signal with the complex conjugateof the known signature sequence to produce a correlation value. When a‘large’ positive correlation results, a “0” is detected, and when a‘large’ negative correlation results, a “1” is detected.

It should be understood that the information bits could also be codedbits, where the code is a block or convolutional code. Also, thesignature sequence can be much longer than a single transmitted symbol,in which case a subsequence of the signature sequence is used to spreadthe information bit.

The prior systems do not properly account for the real world mobilecommunication signals that suffer from signal degradation such asinterference and multipath problems. The systems of the state of the artgenerally tended to make assumptions that all other interferers andmultipaths were additive white Gaussian noise. However, this assumptionis not accurate for co-channel interference and multipaths.

Multipath dispersion occurs when a signal proceeds to the receiver alongnot one but many paths so that the receiver encounters echoes havingdifferent and randomly varying delays and amplitudes. The receiverreceives a composite signal of multiple versions of the transmittedsymbol that have propagated along different paths, called rays, havingdifferent relative time. Each distinguishable ray has a certain relativetime of arrival, a certain amplitude and phase, and as a result, thecorrelator outputs several smaller spikes. RAKE receivers are well knownand attempt to ‘rake’ together all the contributions to detect thetransmitted symbol and recover the information bit.

Conventional RAKE receivers provide satisfactory performance foroperation in the presence of multipath under ideal conditions howeverthe signature sequence must be uncorrelated with time shifted versionsof itself as well as various shifted versions of the signature sequencesof the other CDMA signals. Co-channel interference refers to signalsreceived from other users either directly or reflected. If one receivedsignal corresponding to the signature sequence of interest has anon-negligible cross correlation with the received signal originatingfrom another transmitter (a co-channel interferer), then the valuemeasured at the receiver, e.g. the correlation value for the signal ofinterest, is corrupted. In other words, the correlation computed at thereceiver that would be used to decode a particular signal of interest isoverwhelmed by an interfering signal; this is referred to as thenear-far problem. The interference caused by an echo of one transmittedsymbol overlapping with the next transmitted symbol might also benon-negligible. If this is the case, the transmitted symbols interferewith past and future transmitted symbols. This is commonly referred toas intersymbol interference (ISI). In actuality, performance is degradedboth by co-channel interference and ISI.

There has been much research to address signal interference with knownmultipath time dispersion. This is termed joint demodulation with nomultipath and is further described in S. Verdu, “Minimum Probability ofError For Asynchronous Gaussian Multiple-Access Channels,” IEEE Trans.Info. Theory, Vol. IT-32, pp. 85-96, R. Lupas and S. Verdu, “Linearmultiuser detectors for synchronous code-division multiple-accesschannels,” IEEE Trans. Inform. Theory, Vol. 35, pp. 123-136, January1989; and R. Lupas and S. Verdu, “Near-far resistance of multiuserdetectors in asynchronous channels,” IEEE Trans. Commun., Vol. 38, pp.496-508, April 1990.

There are a host of approaches for jointly demodulating any set ofinterfering digitally modulated signals, including multiple digitallymodulated signals. Maximum Likelihood Sequence Estimation determines themost likely set of transmitted information bits for a plurality ofdigital signals without multipath time dispersion. The maximumlikelihood joint demodulator is capable, in theory, of accommodating thelargest number of interfering signals, but has a prohibitivecomputational complexity that makes it unrealizable in practice. Thedecorrelation receiver is another, less computationally complex receiverprocessing approach that zeroes out or decorrelates the differentsignals so that they no longer interfere with one another. Thedecorrelator as well as virtually every other lower complexity jointdemodulator, is not capable of operation when the number of signals isover a set threshold which falls significantly short of the theoreticalmaximum.

In a real world multi-user system, there are a number of independentusers simultaneously transmitting signals. These transmissions have thereal-time problems of multi-path and co-channel interference, fading,and dispersion that affect the received signals. As described in theprior art, multiple user systems communicate on the same frequency andat the same time by utilizing parameter and channel estimates that areprocessed by a multi-user detector. The output of the optimal multi-userdetector operating within the multiuser capacity limits of the channelis an accurate estimation as to the individual bits for an individualuser.

Moreover, in an article by Paul D. Alexander, Mark C. Reed, John A.Asenstorfer and Christian B. Schlagel in IEEE Transactions onCommunications, vol. 47, number 7, July 1999, entitled “IterativeMulti-User Interference Reduction: Turbo CDMA,” a system is described inwhich multiple users can transmit coded information on the samefrequency at the same time, with the multi-user detection systemseparating the scrambled result into interference-free voice or datastreams.

Low complexity multiuser detector have been contemplated that use linearmultiuser detectors to achieve optimal near-far resistance. (Near-FarResistance of Multiuser Detectors for Coherent Multiuser Communications,R. Lupas, S. Verdu, IEEE Trans. Commun. Vol. 38, no. 4, pp 495-508,April 1990). While providing certain advantages, the performance has notbeen demonstrably improved. Varanasi and Aazhang proposed a multistagetechnique as described in the article Near-Optimum Detection inSynchronous Code-Division Multiple Access Systems, IEEE Trans. Commun.,Vol. 39, No. 5, May 1991.

Decorrelating decision feedback detectors (DDFD) have been described byA. Duel-Hallen in Decorrelating Decision-Feedback Multiuser Detector forSynchronous Code-division Multiple Access Channel, IEEE Trans. Commun.,Vol. 41, pp 285-290, February 1993. Wei and Schlegel proposedsoft-decision feedback to suppress error propagation of the DDFD inSynchronous DS-SSMA with Improved Decorrelating Decision-FeedbackMultiuser Detection, IEEE Trans. Veh. Technol., Vol. 43, pp 767-772,August 1994. Tree-type maximum-likelihood sequence detectors were alsoproposed for multiuser systems as were breadth-first algorithms andsequential detection including using the M-algorithm tree-search schemewith a matched filter (MF). The prior references also reveal schemesthat include some form of decorrelating noise whitening filter (WF).

However, one of the primary disadvantages of the prior referencesimplementations is the inability to accommodate overloaded conditions.Decision feedback techniques are limited in that they are incapable ofworking in supersaturated environments. Although the MMSE-based decisionfeedback detector can work in a supersaturated environment, it has beendemonstrated to be too aggressive with hypothesis testing to produceaccurate results.

Multi-user detection (MUD) refers to the detection of data innon-orthogonal multiplexes. MUD processing increases the number of bitsavailable per chip or signaling dimension for systems havinginterference limited systems. A MUD receiver jointly demodulatesco-channel interfering digital signals. Multiuser detection systems takefull advantage of all information available at the receiver, by makinguse of any “knowledge” that the receiver has about the interferingsignals. Because the number of users that can be packed into a MUD-basedmultiple access (MA) system is a function of the number of independentdimensions over which the set of signals is spread (the dimension of thespan of the set of signals), the total number of users in the system canbe increased if more dimensions are used for transmitting the signalsand the same dimensions are accessible at the receiver.

In addition to expanding the number of dimensions, favorably “spreading”the received signals out over those dimensions can also allow forincreases in the number of users a MUD-based system can accommodate. Forexample, typical signaling sets for multiuser communications do notinclude as a free parameter the reference amplitude of each user. In theIS95 code division protocol, amplitude is controlled completely forpurposes of power control to meet a signal-to-noise specification (allusers ideally being received with the same signal-to-noise ratio (SNR)).Therefore the advantages offered to the MUD are not exploited and theaggregate throughput of a multiple access system is limited if amplitudeis not exploited.

There are various multiuser detectors in the art, including optimal ormaximum likelihood MUD, maximum likelihood sequence estimator formultiple interfering users, successive interference cancellation,TurboMUD or iterative MUD, and various linear algebra based multi-userdetectors such as all of those detailed in the well-known text“Multiuser Detection” by Sergio Verdu. In the state of the art,algebraic means are used to compute linear operators for the entire setof users (communications channels) simultaneously. This is done byutilizing prior information, or knowledge of the likely value of eachuser's bit of information, each at a particular instant in time. Thismultiuser detection processing is described in the text S. Verdu,Multiuser Detection, Cambridge Press, 1998. However, this suffers from asignificant disadvantage in that it requires knowledge of all parametersto perform the processing.

Optimal MUD based on the maximum likelihood sequence estimator operatesby comparing the received signal with the entire number of possibilitiesthat could have resulted, one for each bit or symbol epoch. The numberof possible measured levels for the received signal is exponentiallyrelated to the number of users and the duration of the ISI. Hence, theoptimal processing is a computationally complex and it is not possibleto accomplish in a real-time environment. Thus for those multi-userdetectors that examine the entire space, real-time operation is oftenelusive.

In general, optimal MUD units function by examining a number ofpossibilities for each bit. However, for multi-user detectors thatexamine a larger capacity of signal, the computations are complex andtime-consuming, thus making real-time operation impossible. Numerousattempts at reliable pruning of the optimal MUD decision process or theuse of linear approximation to the replace the optimal MUD have stillnot produced a workable solution for the real world environment.

There are several suboptimal multiuser detectors that are lesscomputationally complex and known in the art. One example of suboptimaldetectors, called linear detectors, includes decorrelators, minimum meansquare error or MMSE detectors, and zero-forcing block linearequalizers. The conventional Minimum Mean Squared Error (MMSE) Multiuserdetector utilizing prior information is described by Wang and Poor in“Iterative (Turbo) Soft Interference Cancellation and Decoding for CodedCDMA”, in the Transactions on Communications, July 1999. See alsoAlexander, Reed, Asenstorfer, and Schlegel, “Iterative MultiuserInterference Reduction: Turbo CDMA,” IEEE Trans on Comm, July 1999; andPoor, “Turbo Multiuser Detection: An Overview” ISSSTA 2000. But, linearalgebra based MUD (non-iterative) and successive interferencecancellation fails for cases of overloaded multiple access systems.

One example of overloading is where the number of simultaneous users isdoubled or tripled relative to existing state of the art. Even forunderloaded multiple access systems, the performance of non-iterativeMUD and successive interference cancellation degrades significantly asthe number of users increases, while the computation complexity of theoptimal MUD increases significantly as the number of users increases.The computing problems are so extreme that even the most expensivehardware unbound by size and weight can often to keep us with thisoverwhelming complex processing requirement of optimal MUD. Moreover, anunreasonable delay would be required to decode each bit or symbolrendering such a system useless in practice.

Reduced complexity approaches based on tree-pruning help to some extentto eliminate the improper bit combination from consideration where,ideally, such a procedure should prune out many ‘bad’ paths in thedecision tree but maintain the proper path. Thus, the entire tree doesnot need to be traversed to make the final decision.

The M-algorithm is a pruning process that limits the number ofhypotheses extended to each stage to a fixed tree width and prunes basedon ranking metrics for all hypotheses and retaining only the M mostlikely hypotheses. The T-algorithm prunes hypotheses by comparing themetrics representing all active hypotheses to a threshold based on themetric corresponding to the most-likely candidate. Performance ofM-algorithm based MUD degrades as the parameter M is decreased, but Mgoverns the number of computations required. Similar effects are seenfor other tree-pruning based MUD (T-algorithm, etc). To combat improperpruning, basic tree-pruning must ensure that M is “large enough”, andtherefore still encounters increased complexity for acceptableperformance levels when the number of interfering signals and/or ISIlengths are moderate to large.

As an illustration of the M-algorithm as a tree-pruning algorithm,consider a tree made up of nodes and branches. Each branch has a weightor metric, and a complete path is sequences of nodes connected bybranches between the root of the tree and its branches. When applied asa short cut to the optimal MUD, each branch weight is a function of thesignature signal of a certain transmitter, the possible bit or symbolvalue associated with that transmitter at that point in time, and theactual received signal which includes all the signals from all theinterfering transmissions. The weight of each path is the sum of thebranch metrics in a complete path. The goal of a tree searchingalgorithm is to try to find the complete path through a tree with thelowest metric. With the present invention the metrics of multiplecomplete paths are not calculated. Rather, the metrics of individualbranches in a tree are calculated in the process of locating onecomplete path through the tree and thereby defines one unknowncharacteristic of each of the co-channel, interfering signals needed todecode the signals.

A MUD algorithm within the TurboMUD system determines discrete estimatesof the transmitted channel symbols, with the estimates then provided toa bank of single-user decoders (one decoder for each user) to recoverthe input bit streams of all transmitted signals. Two general types ofmulti-user detectors within the TurboMUD system are possible, namelythose that provide hard outputs, which are discrete values, and thosethat provide soft outputs, which indicate both the discrete estimate andthe probability that the estimate is correct. In basic terms,turbodecoding refers to breaking a large processing process into smallerpieces and performing iterative processing on the smaller pieces untilthe larger processing is completed, and this basic principle was appliedto the MUD.

However, single-user decoders operating on hard values, or discreteintegers, have unacceptable error rates when there is a large amount ofinterference or noise in the received signal. The reason is thatdiscrete integers do not provide adequate confidence values on which thesingle-user decoder can operate. These decoders operate better onso-called soft inputs in which confidence values can range from −1 to 1,such as for instance 0.75 as opposed to being either −1 or +1. Toprovide soft values that can then be utilized by a single-user decoder,the multi-user detector chosen for the TurboMUD can generate these softvalues. The invention described below will work with soft output or ahard output MUDs, or a combination of the two.

In general, soft or hard output versions of the optimum maximumlikelihood multi-user detector (Verdu, Multiuser Detection, CambridgeUniversity Press, 1998) or an M algorithm (as described, for instance,in Schlegel, Trellis Coding, IEEE Press, 1997) with a moderate to highvalue of M causes the Turbo MUD to require too many computations to keepup with real time transmissions. Using a fast, but inferior, multiuserdetection scheme such as a linear-based detector or those detailed inthe text “Multiuser Detection” by Sergio Verdu causes poor qualityoutput when there are many interferers or users.

Moreover, when dealing with hand-held communications units such aswireless handsets, the amount of processing within the device islimited, directly limiting the amount of computational complexity thatis allowed. In order to provide real-time performance both at a cellsite and the handset, it therefore becomes important to be able toreduce the amount of computational complexity and processing time so asto achieve real-time performance.

A great number of communications and data transfer systems operate at ornear a full capacity. Conventional receiver performance isunsatisfactory in the presence of co-channel interference. Furthermore,many receivers require prior knowledge of signal parameters such asphase and amplitude of the channel to perform processing functionswithout co-channel interference. What is needed is a means to increasethe number of available channels, by reassigning channels to be perhapsslightly interfering, thereby increasing the overall throughput withoutincreasing bandwidth. Such a system should provide an efficient means ofjointly estimating symbols, channel amplitude, and data rate transmittedin a super-saturated communications channel. And, any such inventionshould have the ability to estimate the symbols and data rate, blindlywithout prior knowledge of channel amplitudes and phase.

BRIEF SUMMARY OF THE INVENTION

While adaptable in many forms, one embodiment of the invention is anapparatus for processing a digital data stream from multiple users,comprising an initial amplitude estimation unit processing the datastream and producing initial amplitude estimates on a first iteration.There is a joint amplitude estimator coupled to the data stream and theinitial amplitude estimator, wherein the joint amplitude estimatorproduces updated amplitude estimates. A symbol estimator is coupled tothe data stream, the initial amplitude estimator, and the jointamplitude estimator, wherein the symbol estimator produces a pluralityof symbols estimates for each user. There is a bank of decoders coupledto the symbol estimator, producing a plurality of symbol likelihoodestimates for each user, wherein the symbol likelihood estimates areiteratively fed back to the symbol estimator and the joint amplitudeestimator until a final condition is obtained.

Further variations of the invention include wherein the final conditionis selected from at least one of the group consisting of: bit error ratemetric level and fixed number of iterations.

The symbol hypothesis testing module can generally be any module that isa member selected from at least one of the group consisting Minimum MeanSquared Error (MMSE), maximum likelihood, M-algorithm, T-algorithm, andQ-algorithm, decorrelating decision-feedback detector (DDFD), improveddecorrelating decision-feedback detector (IDDFD), successiveinterference cancellation (SIC), parallel interference cancellation(PIC) and multi-stage detector; block-iterative interferencecancellation; and a deferred decorrelating decision-feedback detector.

The apparatus can further comprise an energy detector coupled to thejoint amplitude estimator which can determine activity on a channel,such as code, time slot or frequency channel.

The bank of decoders can be selected from at least one decoder of thegroup consisting of: Soft-output Viterbi, Maximum A Posteriori, andBCJR.

Another embodiment includes a method for providing initial amplitudeestimation for a plurality of user channels, comprising separating theuser channels into active channels and inactive channels. Processing theinactive channels is according to the sub-steps comprising applying abank of filters to the inactive channels for each inactive user,squaring an output from the filters, summing an output from the squaringoperation, and calculating an average bias estimate from an output ofthe summing. Processing the active channels is according to thesub-steps comprising applying a bank of filters to the user data of theactive channels, squaring an output from the bank of filters, summing anoutput from the squaring, and removing the average bias estimate from anoutput from the summing.

The method can include processing the inactive channels by dividing theinactive channels into groups and calculating at least one group averagebias estimate. It can also include processing the active channels bydividing the active channels into groups and respectively removing theat least one group average bias estimate.

A further variation includes scaling by the summers and may include anenergy detector for separating the active channel from the inactivechannels.

Another embodiment of the invention is an apparatus for processinginitial amplitude estimates from a data stream of multiple users,comprising an energy detector measuring an energy level of the datastream, wherein the energy detector separates a plurality of activechannels from a plurality of inactive channels. There is a bank offilters coupled to the inactive channels, a bank of squaring operatorscoupled to the bank of filters, a bank of inactive summers coupled tothe squaring operators, and an average bias estimator coupled to theinactive summers calculating an average bias estimate. There is also abank of filters coupled to the active channels, a bank of squaringoperators coupled to the bank of filters, a bank of active summerscoupled to the squaring operators, and a bias removal section coupled tothe active summers and the average bias estimator.

Another embodiment includes a joint amplitude estimator for a datastream from multiple users, comprising a data stream from the multipleusers divided into a plurality of observation intervals. There are aplurality of processing modules coupled to the observation intervals,wherein the processing modules calculates interference cancellationvalues for each of the users and computes a filter for each of theobservation intervals. The filter being applied to the data within theobservation interval is used to compute individual amplitude estimates.There is an amplitude estimation unit that processes the individualamplitude estimates and calculates new amplitude estimates, wherein thenew amplitude estimates are iteratively passed back to the processingmodules until a final condition is obtained.

One aspect includes where the amplitude estimation unit sums and weighsthe individual estimates such that the new amplitude estimate is aweighted average of individual amplitude estimates.

It should be understood that the observation intervals may be based ondistinguishing attributes selected from at least one of the groupconsisting of: time, code, and frequency.

The filter may be calculated for an arbitrary observation interval m bythe equation:G _(m) =B _(m) S _(m) ^(H)(S _(m)χ_(m) S _(m) ^(H)+σ_(n) ² I)⁻¹.

Yet a further embodiment includes a method for processing amplitudeestimates for a multiuser data stream divided into a plurality ofobservation intervals, comprising computing a filter for eachobservation interval of the data stream, applying interferencecancellation to the data stream for each observation interval, applyingthe filter to the data stream for each observation interval from theinterference cancellation to produce individual amplitude estimates foreach observation interval, computing new amplitude estimates using theindividual amplitude estimates; and passing the new amplitude estimatesback to the filter for iterative processing until a final condition isobtained.

One embodiment is a turbo-decoding system for joint signal demodulation,comprising a data stream from a plurality of users divided into aplurality of observation intervals. There is a plurality of symbolprocessing nodes processing the observation intervals to compute symbolestimates for the data stream within the observation interval. Aplurality of decoder nodes is present for processing the symbolestimates and producing a plurality of symbol likelihoods. There is aplurality of amplitude update nodes processing the symbol likelihoodsand calculating a plurality of amplitude update vectors. And, there isan amplitude estimator node processing the amplitude update vectors andproducing an amplitude estimate update, wherein the amplitude estimateupdate is passed back to the processing nodes for iterative processingbetween the symbol processing nodes, the decoder nodes, the amplitudeupdate nodes, and the amplitude estimator node until a final conditionis obtained.

The turbo-decoding system can encompass a plurality of memory units,wherein at least one memory unit is coupled to each of the processingnodes and to each of the amplitude estimation nodes. There can also be afirst system area network (SAN) coupling the processing nodes and thedecoder nodes, a second system area network (SAN) coupling the decodernodes and the amplitude estimation nodes, a third system area networkcoupling the amplitude estimation nodes and the amplitude estimator, anda fourth system area network coupling the amplitude estimator and theprocessing nodes. The processing nodes, the decoder nodes and theamplitude estimation nodes can even be one set of nodes. A set of nodescan also be comprised of at least one processor.

A further embodiment of the system includes wherein the likelihoodsymbols from the decoders are accessible at any time forpost-processing. The processing nodes can further comprise a thresholderto convert the symbol estimates to symbol bits. And, the bank of decodercan also perform de-interleaving, rate identification, de-puncturing andde-scrambling.

The features and advantages described herein are not all-inclusive and,in particular, many additional features and advantages will be apparentto one of ordinary skill in the art in view of the drawings,specification, and claims. Moreover, it should be noted that thelanguage used in the specification has been principally selected forreadability and instructional purposes, and not to limit the scope ofthe inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a basic diagrammatic processing perspective for theprior art transmitter end and receiver end with iterative multiuserdetection processing;

FIG. 2 is a block diagram perspective showing joint symbol amplitude anddata rate estimator for one embodiment of the invention;

FIG. 3 is a schematic block diagram showing initial amplitudeestimation;

FIG. 4 is a schematic block diagram showing joint amplitude estimator;

FIG. 5 is a schematic block diagram showing parallelization of jointsymbol amplitude and data rate estimator; and

FIG. 6 is a schematic block diagram showing hardware conceptualizationaccording to one embodiment of the present invention with nodes used todepict the processing.

DETAILED DESCRIPTION OF THE INVENTION

The methods and embodiments of the joint symbol, amplitude and rateestimator disclosed herein enable implementations of advanced receiverprocessing providing high quality real-time processing for multipleaccess systems, including overloaded situations. One embodimentillustrates the signal processing technique applicable to manyvariations that are all within the scope of the invention. It should beunderstood that the reference to a data stream of multiple users isunderstood to represent digital data from a number of references and notnecessarily multiple users in communications.

In order to fully appreciate the processing of the present invention, itis useful to consider a description of the processing known in the art.Referring to FIG. 1, prior art transmitter section 5 and receiversection 10 is depicted with MUD processing. There are K users with datad₁-d_(k) as the input to the channel encoders 20 where the data isconvolutionally encoded at a code rate R_(k). The interleaver 25performs the interleaving of the data, which is then symbol mapped bythe symbol mapper 30, such as BPSK symbol mapped with data symbolshaving a duration T. Each data symbol is then modulated by a spreadingwaveform from the spreader 35, and the signal data s_(k)(t) is outputfrom the transmitter end with a number of data symbols per user perframe. In the typical wireless communication application presentedherein, a number of users (1-K) generate signals that are transmitted byantennas 40 into free space. The transmission on hard-wire or fixed wiresystems is also known in the art, however the present example isdirected towards the wireless communications systems. Noise n(t), suchas white Gaussian noise, is an inherent component of most transmissionsignals and some random noise components are generally present intransmitted data signals r(t).

On the receiving end 10, antenna(s) 42 receive the transmitted signalsr(t) as well as the various interfering signals, such as n(t). There isnormally a noise component n(t) that is introduced from the environmentof a random nature in the received signal. While any noise that has arepeatable or non-random nature can be eliminated through processing,random noise elements are reduced in other manners such as filtering butare inherent conditions. The various signals are received at antennas 42wherein there is typically one signal for each polarization feed. Thereceived signals represent directly received signals, as well asmulti-path signals from the same user, and interfering signals fromother users.

The input signals of raw non-manipulated data at the receiver 10 iscomprised of the aggregate of many signals from many differenttransmitters/users, where each signal is generally assigned a channel(frequency, timeslot, and/or spreading code) from a finite set ofchannels. The interference from these various users generally requirescomplex processing and associated processing time. The input datarepresents a vector of data, transferred at some rate (e.g., the symbolrate), and this data is typically transmitted to a matched filter (notshown).

The plurality of signals from each antenna 42 is processed in a RF frontend unit 44. The front end unit 44 generally downconverts the higherfrequency signals into baseband signals and provide processing andfiltering as is known in the art. The baseband signals are alsodigitized by analog to digital converters (ADC). The front end 44cooperates with the parameter estimation unit 46 to retrieve neededinformation for the signals such as relative received timing offsets,carrier phase, frequency offsets, received amplitudes, and multipathstructure for each of the interfering signals present in the receivedsignal.

A parameter estimation unit 46 is coupled to the front end 44 andprocesses the various parameters for the received vector data. Aparameter estimator 46, as known in the art, is a broad description fora unit that provides information to the MUD 50 such as convolutionalcode, signatures, and multiplexing format. While the term parameterestimator is used herein, the term is intended to be interpreted in thebroader sense as known in the joint demodulation field. The parameterestimation module 46 generally tries to estimate timing, signalamplitudes, phases, polarization, and identification of transmissionchannels.

The MUD element 50 consists of functional blocks that process thedigital data and extract the user symbol streams. The MUD 50 performspre-processing and converts the baseband digital data into the properformat for further processing according to the detection scheme. Theformat is often one measurement per ‘dimension’ per symbol. The MUD unit50 outputs a bit (or symbol) stream associated with each interferingsignals present on the channel for one data block.

Turbo MUD processing assumes knowledge of various parameters such asrelative received timing offsets, carrier phase, frequency offsets,received amplitudes, and multipath structure for each of the interferingsignals present in the received signal. This input data represents rawdata after some front end processing such as downconversion,amplification, and analog-to-digital conversion. MUD 50 needs some rawdata parameters in order to establish accurate decision trees forprocessing.

There is an iterative processing topology with a MUD section 50 thatinteracts with the K number of channel decoders 65 for the receivedsignal r(t) for each user. The multiuser detector 50 is generally a SISOdetector, receiving and outputting soft values, although hard valueprocessing is known in the art. The MUD section 50 utilizes a prioriinformation and delivers values such as a posteriori log-likelihoodratio (LLR) data of a transmitted ‘+1’ or ‘−1’ for each bit of everyuser. Certain a priori information is computed by the channel decoder 65from a previous iteration which is interleaved and fed back to the MUDsection 50. The interleaver 60 and deinterleaver 75 perform theinterleaving and deinterleaving functions respectfully for the encodingschema. Likewise, the summers 55, 70 perform the summations for thedata. Once the processing is complete the channel decoders 65 producethe output data stream (dk)′ representing the best estimate of thetransmitted signal (dk).

Various schemes for TurboMUD processing are well known in the art andutilize a priori information about the received signals wherein theprocessing continues through a number of iterations until certainconditional parameters are satisfied. The basic Turbo-Mud procedure ispresented in published literature such as Poor, “Turbo MultiuserDetection: An overview,” IEEE 6^(th) Int. Symp. On Spread-Spectrum Tech.And Appli., NJIT, New Jersey, Sep. 6-8, 2000 and Alexander, Reed,Asenstorfer, and Schlegel, “Iterative Multiuser Interference Reduction:Turbo CDMA,” IEEE Trans. On Comms., v41, n7, July 1999. The iterativeMUD algorithm such as representative of the approaches used toincorporate turbo decoding methods into joint MUD/FEC (Fourier ErrorCorrection) decoding and to then reduce the complexity of the system areknown in the art.

The bit streams from the MUD 50 are passed to a bank of error correctiondecoders unit 65. The decoders 65 calculate conditional probabilities,one for each decoded symbol of each user, and output them as confidencevalues back to the MUD 50. In one embodiment, there is a bank of errorcorrection decoders 65 such as Viterbi decoders or MAP.

Soft outputs for each bit of each user from the bank of decoders 65 isfed back to the multiuser detector 50 for each iteration, one stream ofoutputs for each interfering user present in the received signal. Thesesoft outputs are passed back to the MUD 50 to produce an improved streamof soft bit (or symbol) decisions that fed into the decoder 65 foranother iteration of improvement. The information between the MUD 50 andthe decoders 65 repeats in subsequent iterations until an asymptote isreached or the desired performance level is attained. At that point,estimates of the data sequences for all active users are output.Operation then commences for the next block of data, repeating theprocess described above. The multiuser detector 50 takes these softinputs along with the original raw input signal to calculate animproved, less corrupted bit stream for each user. This iterativeprocess continues until a desired metric is reached or a fixed number isreached. At that point, estimates of the data sequences for all activeusers are output. Operation then commences for the next block of data,repeating the process described above.

The number of iterations for processing between the MUD 50 and thedecoders 65 can be set to a fixed counter or by checking if there weresignificant changes to the data from the last iteration. Once the datais no longer being altered or reaches a certain iteration counter limit,the data from the decoder 65 can be output as final estimates of whatthe user sent, (dk)′. A fixed number of iterations can be stored andused and processed by the decision block. Alternatively, the informationbetween the multiuser detector 50 and the decoders 65 repeats insubsequent iterations until an asymptote is reached or the desiredperformance level is attained. A buffer can store the previous valuesand compare them to the latter processed values during the iterativeprocess.

MMSE detectors with prior information are usually described in a turboor iterative application, but also function in non-iterative operations.As described herein, a complete solution to the problem is computed, andone approximate (low complexity) method is described. Furthermore, priorschemes are generally inferior for overloaded systems, and this isalleviated by the present invention.

The apparatus in FIG. 2 is now described in more detail. A Data Stream100, potentially complex, is received from some source. For the case ofCode Division Multiple Access (CDMA) communications schemes, the datastream is sampled by some multiple of the chip rate. For TDMAcommunication schemes, the data stream is sampled at some multiple ofthe symbol rate. The received signal can effectively be modeled as thelinear combination of many co-channel signals, mathematicallyillustrated by

$\begin{matrix}{{r\lbrack n\rbrack} = {{\sum\limits_{i = 1}^{F}{\sum\limits_{k = 1}^{K}{{b_{k}\lbrack i\rbrack}a_{k}{s_{k}( {{nT}_{n} - {iT}_{i}} )}}}} + {{n_{w}( {nT}_{n} )}.}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

The term b_(k)[i] represents the symbol for user k at time index i. Theterm s_(k)(nT_(n)-iT_(i)) represents the user's signal characteristicfor sample index n for the symbol period i for the user k. Thecharacteristic waveform, s_(k)(•), is normalized such that the amplitudeof the transmitted signal for user k is represented by a_(k). The symbolperiod is defined by T_(i) and the sample period is represented byT_(n). The linear model represents a summation of K separate users andover F symbols. The received signal can represent a collection frommultiple sources. For example, the collection may consist of multipleantennas, not necessarily co-located, with potentially differentpolarizations.

The user's signal characteristic, s_(k)(nT_(n)-iT_(i)), combines asequence of signal transformations: pulse shaping filter, signal delays,receiver filters, code channels (for CDMA) and multipath. It should beunderstood that the characteristic waveform is not restricted to asample or symbol interval.

The multiple access interference model in Equation 1 is representedconcisely using matrix notation in Equation 2. The KF symbols,corresponding to the KF simultaneous users, is represented by theKF-element vector, b[i] at time i. A linear model is used to representthe received signal at time index i byr[i]=SAb[i]+n _(w) [i],  Equation 2where b[i] represents the KF symbols at time index i. The term S is aN×KF matrix representing the combination of the spreading code, channelcodes, pulse shaping filter, and propagation effects

$\begin{matrix}{S = \begin{bmatrix}{s_{1}\lbrack {1,i} \rbrack} & {s_{2}\lbrack {1,i} \rbrack} & \cdots & {s_{KF}\lbrack {1,i} \rbrack} \\{s_{1}\lbrack {2,i} \rbrack} & {s_{2}\lbrack {2,i} \rbrack} & \cdots & {s_{KF}\lbrack {2,i} \rbrack} \\\vdots & \vdots & \ddots & \vdots \\{s_{1}\lbrack {N,i} \rbrack} & {s_{2}\lbrack {N,i} \rbrack} & \cdots & {s_{KF}\lbrack {N,i} \rbrack}\end{bmatrix}} & {{{Equation}\mspace{14mu} 3}\;}\end{matrix}$

The matrix entries, s_(k)[n,i], represents the n^(th) sample of thesignal characteristic waveform for user k at time i. The term A is aKF×KF diagonal matrix that represents the complex signal amplitudes,which can include phase and polarization weighting. The term n_(w)[i] isa N×1 vector that represents additive white Gaussian noise (AWGN).

Referring again to FIG. 2, the data stream 100 represents a vector ofdata, representing the received signal. This data is communicated to thesymbol hypothesis testing module 105 which is indicated in thisimplementation as Minimum Mean Squared Error (MMSE) based symbolestimator. The MMSE implementation for symbol hypothesis testing 105 isbetter for computational efficiency however other forms of symbolestimators such as the maximum likelihood detector are within the scopeof the invention. The maximum likelihood detector is a brute forceapproach which performs an exhaustive evaluation of the Euclideandistances between the received samples and the linear model of thesamples using every possible hypothesis of the bit sequence. Instead ofthe optimal detector for the symbol estimation testing module 105, morecomputationally efficient forms of symbol hypothesis testing includes,the M-algorithm, T-algorithm, etc. Although the most efficient approachappears to be the MMSE-based symbol estimator, another efficientimplementation for symbol estimation is referred to as the Q-algorithm,disclosed in U.S. patent application Ser. No. 10/105,918, filed Mar. 25,2002 entitled “System for Decreasing Processing Time in an IterativeMulti-User Detector System”. The Q-algorithm used for the MMSE-symbolestimator cancels interference based on symbol estimates obtained fromthresholding the symbol likelihoods.

The computational efficiency utilizing the MMSE based symbol estimationis described in further details for the MMSE-based joint amplitudeestimation and the parallelization of the algorithm. It should be notedthat the symbol hypothesis testing is performed following completion ofthe initial amplitude estimate.

The same data vector 100 is passed to the initial amplitude estimationmodule 110. As indicated by the name, the purpose of initial amplitudeestimation module 110 is to provide an initial estimate of the amplitudefor each interfering signal. The approach implemented in initialamplitude estimation module 110 consists of incoherently integrating theenergy out of a properly normalized matched filter for all usersdetermined to be active. Note these users can be transmitted over code,temporal, and/or frequency channels and this estimation procedure areappropriate for these modes of digital communication. Part of thefunction performed in the initial amplitude estimation module 110, isthe identification of which users are active. User identification can beeasily obtained via an energy detector based on some sufficiently longobservation interval that can determine activity on a channel: code,time slot, or frequency channel. The energy detector is generallyintegrated within the initial amplitude estimation module 110.

The initial amplitudes from the estimator 110 are passed to theMMSE-based symbol estimator 105 and the MMSE-based Joint AmplitudeEstimator 120. The MMSE-based symbol estimator 105 uses the initialamplitude estimate only on the first iteration, for all other iterationsit uses the amplitude estimates from the MMSE-based Joint AmplitudeEstimator 120. This module may implement any type of symbol hypothesistesting procedure however an MMSE-based approach is used because ofcomputational efficiency. It should be understood that other lineardetectors, such as the decorrelator, may also be used for computationalefficiency. While the linear detectors are efficient, the performance issacrificed through BER performance and loading capacity.

There are numerous non-linear symbol-hypothesis testing proceduresadaptable to the present invention, including theM-algorithm/T-algorithm; maximum likelihood; decorrelatingdecision-feedback detector (DDFD) and improved decorrelatingdecision-feedback detector (IDDFD); successive interference cancellation(SIC), parallel interference cancellation (PIC) or multi-stage detector;block-iterative interference cancellation. A further symbol-hypothesistesting implementation is described in the commonly owned applicationentitled “Deferred Decorrelating Decision-Feedback Detector forSupersaturated Communications”, U.S. patent application Ser. No.10/423,655, filed Apr. 25, 2003.

The MMSE-based symbol estimator 105 provides symbols estimates to a bankof single user decoders 130. The symbols estimates from the symbolestimator 105 are routed to the decoders 130 based on user number. Forexample, all symbol likelihoods corresponding to the first user arepassed to the decoder dedicated to the first user. This mapping isrepeated for all users. The single user decoders consist of a bank of Kdecoders that may consist of Soft-output Viterbi or Maximum A Posterioribased techniques such as BCJR. Soft-input, soft-output (SISO) decodersare used in one embodiment for this implementation, but hard values canbe processed as is known in the art.

Inherent in most processing is a de-interleaving, rate identification,de-puncturing and de-scrambling processing depicted merely asdeinterleaving 125 for illustrative purposes. The symbol estimates andtheir respective likelihoods are output from the bank of decoders 130for one of the most-likely data rates. In addition, symbol likelihoodestimates from the decoders 130 are also fed back around to both theMMSE-based symbol estimator 105 and MMSE-based amplitude estimator 120respectively. However, prior to feedback the symbol likelihoods areprocessed by an interleaving section 135 performing re-interleaving,re-puncturing, re-scrambling and symbol repeating (for low rate frames).The purpose of this re-coding task is to properly emulate theinterference.

As referenced herein, the term ‘data rate’ refers to the rate at whichthe information bits are transmitted over the channel, defined in termsof bits per second. Sometime the data can be transmitted at one of anumber of different rates. The likelihoods of the bits are maximizedwhen the data rate is known. However, if the receiver isnon-cooperative, the ‘most likely’ data rate must be identified. Oneapproach for determining the most likely data rate is through jointmaximization of the bit likelihoods and data rate.

The new symbol likelihoods from the decoders 130 are used to updateprevious amplitude estimates in the joint amplitude estimator 120. Theprocedure for processing the MMSE-based amplitude estimator is describedin further detail herein. The new amplitude estimates from the jointamplitude estimator 120 are passed to the MMSE-based Symbol Estimator105 that re-estimates the symbols based on prior information on thesymbols and the updated parameter estimates. These updated symbolestimates from the symbol estimator 105 are then re-routed to the bankof decoders 130. This process is repeated for either some fixed numberof iterations or until the estimates of the bit error rate or frameerror rate achieve some desired level.

FIG. 3 illustrates the system and processing for calculating initialamplitude estimates with the Initial Amplitude Estimator 110 of FIG. 2.Referring to FIG. 2 and FIG. 3, the initial amplitude estimate obtainedfrom the Initial Amplitude Estimator 110 is based on the incoherentintegration of the matched filter output of the Active Channel 180 oversome observation interval. The procedure consists of applying a bank ofmatched filters 205, 230, 255 for all users K and all symbols F to thereceived data vector 100. The KF×1 vector representing the matchedfilter outputs is represented byy=S ^(H) r=S ^(H) SAb+S ^(H) n _(w).  Equation 4

The estimate of the amplitude for a user k is based on summing thesquared outputs of the matched filter for that user k, summed over anobservation interval of F symbols. An estimate of the amplitude squaredis represented by

$\begin{matrix}{{\hat{a}}_{k}^{2} = {\frac{1}{F}{\sum\limits_{i = 1}^{F}{y_{i,k}}^{2}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

The summation over F is performed for all K users to obtain K initialamplitude estimates for the K users. Since the estimate is based onincoherent integration, the estimate is biased which is illustrated bythe expected value of the initial amplitude estimate for an arbitraryusers k,

$\begin{matrix}{{E\lbrack {\hat{a}}_{k}^{2} \rbrack} = {a_{k}^{2} + {\frac{1}{F}{\sum\limits_{i = 1}^{F}{\sum\limits_{\underset{j \neq k}{j = 1}}^{K}{{\rho_{j,k}^{2}(i)}a_{j}^{2}}}}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$where the bias is represented by the last term in Equation 6 and ρ_(j,k)²(i)=(s_(k) ^(H)(i)s_(j)(i))². Note that the bias in Equation 6 is basedon the expected values of the bits in the vector b, in Equation 4,equaling zero. This is mathematically represented byE[b]=0In addition, Equation 6 assumes the symbols are defined such that b_(i)²=1. A generalization of Equation 6 for the case of non-zero mean bitvalues and b_(i) ²≠1 is obvious.

An inactive user 190 is defined as a signature signal that is notpresent. The expected value of the amplitude estimate for an arbitraryinactive channel is defined as

$\begin{matrix}{{E\lbrack {\hat{a}}_{inactive}^{2} \rbrack} = {\frac{1}{F}{\sum\limits_{i = 1}^{F}{\sum\limits_{j = 1}^{K}{{\rho_{j,{inacive}}^{2}(i)}a_{j}^{2}}}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$where ρ_(j,inactive) ²(i)=(s_(inactive) ^(H)(i)s_(j)(i))². Since nosignal is present for inactive users, a nonzero value for the expectedvalue of the estimate for amplitude squared for inactive signaturevectors is due to the presence of other active channels. ComparingEquations 6 and 7 reveals that examination of the estimate of squaredamplitudes for non-active users can be used to estimate the biasrelative to active users. Thus the Initial Amplitude Estimation conceptis based on Equations 5 through 7 is illustrated in FIG. 3.

A data stream 200 representing the received signal, potentially complex,is received from some source. A bank of filters 205, 230, 255, 280 and300 is applied to the data stream 100. The Active Channel 180 set offilters 205, 230, 255 consists of filters matched to users that havebeen determined to be active based on an energy detector. Filter 205corresponds to a filter matched to the signature signal for user 1.Similarly, filter 230 corresponds to a filter matched to the signaturesignal for user 2. It should be noted that the size of the bank ofmatched filters is based on the number of users determined to be activeand represented by the term K.

The output data from each matched filter (1-K) 205, 230, 255 is thenprocessed by a squaring operation 210, 235, 260 for all K users. Theoutputs from the squaring operations 210, 235, 260 for each activechannel user (1-K) are then passed to a bank of summers 215, 240, 265which performs the incoherent integration. The bank of summer 215, 240,265 for each user 1-K used to implement Equation 5, accumulates energyin each of their respective processing chains. The proper scalingrequired for averaging is included in the output of the summers. Aspreviously mentioned these estimates of squared amplitude are biased,therefore, the bias removal component must be implemented to minimizethe bias.

A set of filters 280, 300 for the Inactive Channel 190 representingthose channels that were determined to be inactive based on an energydetector. The bank of filters 280, 300 matched to the inactive user'ssignature signals is applied to the data stream 200 and processed. Thesize of the bank of filters 280, 300 is based on the number of usersdetermined to be inactive, defined herein as the number J.

Following the processing of the inactive filters 280, 300 for the users1-J, the user data is further processed by a squaring operation 285, 305for all J users. The outputs from the squaring operations 285, 305 foreach inactive channel user (1-J) are then passed to a bank of summers290, 310. The bank of summer 290, 310 for each user 1-J accumulatesenergy in each of their respective processing chains. The proper scalingrequired for averaging is included in the output of the summers 290,310, wherein the output is then processed by the Average Bias Estimator295.

Following the computation of the amplitude bias by the Average BiasEstimator 295, the bias is passed to Bias Removal 220, 245, 270 that isapplied to each of the Active Channel Users 1-K. The illustration inFIG. 3 shows the output from the active channel summers 215, 240, 265going to all bias removal modules 220, 245, 270. The initial amplitudesquared estimate obtained for the active channels has an estimate of thebias removed in modules 220, 245, 270. Following the bias removal, theinitial amplitude squared estimates 225, 250, 275 for each user 1-K arepassed out to the remaining processing shown in FIG. 2.

FIG. 3 shows the computation of one amplitude bias in module 295, whichis then applied to all bias-removal modules 220, 245, and 270, for allactive channels. The processing configuration of FIG. 3 is alsoextendable to other embodiments in which different biases are computedfor different sets of active channels, which are obvious extensions andwithin the scope of the present invention. For example, in a wirelesscommunications system the bias removal could be restricted to channelswithin certain sectors. More specifically, for all non-active channelsidentified in a sector, a bias estimate is computed and then applied toall active channels in the same sector. Therefore, active channelsidentified in a different sector will only use a bias based onnon-active channels in this same sector.

Referring to FIG. 2, the initial amplitude squared estimates are updatedin the MMSE-based Joint Amplitude Estimator 120. The apparatus forupdating these amplitude squared estimates is described in FIG. 4. Thesolution described in FIG. 4 is based on the assumption that theamplitude is nearly constant over some observation window. Since thissolution is based on the MMSE criteria, this approach is easily extendedto time-varying amplitudes, however, only the windowed based estimatoris detailed herein for illustrative purposes. For example, when theamplitude fluctuates by rate, the amplitudes over multiple frames arelinked based upon the data rate. Incorporating data rate into theamplitude estimator, the module described in FIG. 4 actually evolvesinto an energy-per-bit estimator. When the amplitude fluctuates based ontemporal instability, then this implementation is extended into a Kalmanfilter framework by using a temporal model of the channel variation. TheKalman filter equation itself is well known to those in the art.

Referring to FIG. 2 and FIG. 4, the stream of data representing thereceived signal 100 is passed to the MMSE-based Joint AmplitudeEstimator 120. This data stream 100 is divided up into M observationintervals as illustrated by r₁, r₂, through r_(M) 400, 405, 410. Theseintervals can be based on distinguishing attributes such as time, code,or frequency. For the example presented herein, the observation intervalrefers to time. These observation intervals each enter a differentprocessing section 415, 420, 430, representing 1 to M observationintervals. In each processing module, 415, 420, 430, an MMSE filter iscomputed which is defined for an arbitrary observation interval m as:G _(m) =B _(m) S _(m) ^(H)(S _(m)χ_(m) S _(m) ^(H)+σ_(n) ²I)⁻¹.  Equation 8

The term B_(m) refers to a diagonal matrix representing the expected bitor symbol value in interval m for all K users. For example,

$\begin{matrix}{{B_{m} = \begin{bmatrix}{\hat{b}}_{1} & 0 & \cdots & 0 \\0 & {\hat{b}}_{2} & \cdots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \cdots & {\hat{b}}_{K}\end{bmatrix}},} & {{Equation}\mspace{14mu} 9}\end{matrix}$where {circumflex over (b)}_(k) represents the expected value of thesymbol for user k. For illustrative purposes, for BPSK systems,{circumflex over (b)}_(k)=2p_(k)−1, where p_(k) equals the probabilitythat the symbol equals 1. The term S_(m), is similar to that presentedin Equation 2, except here S_(m) represents the “S-matrix” defined overa single observation interval, while S in Equation 2 is defined over Fobservation intervals. Therefore, S in Equation 2 contains the S_(m)defined herein. The term χ_(m) represents the amplitude uncertainty andis rewritten as:χ_(m)=(Ω+A ²)Λ_(m)  Equation 10which more clearly defined the amplitude uncertainty in terms of the bituncertainty, Λ_(m), the initial amplitude uncertainty, Ω, and theexpected value of the amplitude A. The terms, Λ_(m), Ω, and A are alldiagonal matrices, and each element along the diagonal corresponding toa different active user, therefore, χ_(m) is a diagonal matrix. Thisformulation assumes the amplitudes and corresponding uncertainties areconstant for the M observation intervals.

Following the computation of M different MMSE filters in each of the 1-Mmodules 415, 420, 430, each MMSE filter is applied to components of therespective data stream 400, 405, 410 corresponding to the sameobservation interval.

Prior to application of the MMSE filter, interference cancellation isperformed based on the expected value of the bits, represented by B_(m)and prior estimates of the amplitude for all K users, represented by theK×1 vector a^(l−1). The interference cancellation is represented by{tilde over (r)} _(m) =r _(m) −S _(m) B _(m) a ^(l−1).  Equation 11And, each MMSE processor cancels out different interference.

Therefore, application of the MMSE filter following interferencecancellation is represented byΔa _(m) =G _(m)(r _(m) −S _(m) B _(m) a ^(l−1))  Equation 12.

The amplitude estimator is defined in module 435 of FIG. 4. It is aniterative formulation that updates previous estimates, represented bya^(l−1), with information based on new estimates of the users' bits anddata rate. These new estimates are represented by a^(l). The MMSE-basedjoint amplitude estimator 435 combines the individual estimates, Δa_(m),in a weighted manner. The weighted combination in this embodiment isexpressed by:

$\begin{matrix}{{\underset{\_}{a}}^{l} = {{\underset{\_}{a}}^{l - 1} + {( {{\Omega^{1/2}( {Q + I} )}^{- 1}\Omega^{1/2}} ){\sum\limits_{m = 1}^{M}{\Delta\;{\underset{\_}{a}}_{m}\mspace{14mu}{where}}}}}} & {{Equation}\mspace{14mu} 13} \\{Q = {\sum\limits_{m = 1}^{M}Q_{m}}} & {{Equation}\mspace{14mu} 14}\end{matrix}$

As indicated by Equation 13 and illustrated in FIG. 4, the individualestimates from the filter modules 415, 420, 430 are passed to theAmplitude Estimator 435 for each interval 1-M. These individualestimates, Δa_(m), are K×1 vectors which are then summed together inAmplitude Estimator 435. Theses estimates are then weighted by(Ω^(1/2)(Q+I)⁻¹Ω^(1/2)) where Q^(1/2) is a diagonal matrix containingthe standard deviation of the amplitude estimates and Q is representedin Equation 14. The term Q is a summation of quadratics computed in thefilter modules 415, 420, 430. Each quadratic is computed byQ_(m)=G_(m)S_(m)B_(m)  Equation 15which is shown to be based on the MMSE filter for the respectiveobservation region defined in interval m. Therefore each filter module415, 420, 430 responsible for an observation interval must compute: theMMSE filter (see Equation 8); apply interference cancellation to thesection of the data stream corresponding to the observation interval(see Equation 11); apply the MMSE filter to the interference mitigateddata vector (see Equation 12); and computed the quadratic (see Equation15).

In this embodiment the outputs of each module 415, 420, 430 is a K×1vector Δa_(m) and a hermitian matrix Q_(m) which is a K×K matrix withK(K+1)/2 unique elements. These two outputs are passed to the AmplitudeEstimator 435 for each interval 1-M. The prior amplitude estimates,a^(l−1) are updated to a^(l) and passed out from the Amplitude Estimator435 back to the individual filter modules 415, 420, 430 defined for theobservation intervals. This feedback is part of the iterativeimplementation inherent in this MMSE formulation.

Referring again to FIG. 2, it should be understood that the amplitudeestimates are not updated until the bits (or symbols) have been updatedin the bank of decoders 130. Following the processing by the MMSE-basedsymbol estimator 105 and the decoders 130 in FIG. 2, the symbols are fedback to the MMSE-based joint amplitude estimator 120 where prioramplitude estimates are updated as detailed herein. As previouslydescribed, the number of iterations is typically dictated by the userwhich is defined for either a fixed iteration number or until the frameerror rate or estimate bit error rate metric reaches some desired level.

Once processing is completed, the updated amplitude estimate vector 440is transmitted from the MMSE-based Joint Amplitude Estimator 120 to theMMSE-based symbol estimator 105. Since for this particularimplementation, the amplitudes are constant over the observationinterval described for M smaller observation intervals, the sameamplitude estimate 440 is reported to each MMSE-based symbol estimator105. This approach is easily modified for a variable amplitude estimatewhich varies over the entire observation interval.

The apparatus in FIG. 5 is one embodiment of a hardware implementationthat provides a more detailed characterization of the processing shownin FIG. 2. It should be noted that FIG. 5 shows an implementation thatparallels the joint estimation based on M observation intervals. Thedata stream in 100 of FIG. 2 is partitioned into M observation intervalsas the data vectors 500, 502, 504, 506 representing intervals 1-M. As inprevious diagrams, the number of observation intervals is generalized toM intervals. The observation intervals may overlap (not necessarilydisjoint) to account for asynchronous reception and multipath signals.

These data vectors 500, 502, 504, 506 are processed immediately by abank of MMSE filters which are implemented on the M processors, 520,522, 524, 526, for all M observation intervals and each processorsimultaneously estimates symbols for all K users. The filter processingcan be considered a node, wherein a node is a generic processingcomponent as is known in the art, such as a microprocessor chip orprocessing board, and wherein it should be readily apparent that therecan be more than one processor per node.

In addition, these data vectors 500, 502, 504, 506 are read into localmemory 510, 512, 514, 516 on the processors, because they are usedrepeatedly for the multiple iterations in this iterative approach tosymbol, amplitude, and rate estimation. Any of the forms of memory knownin the art are applicable.

Each symbol estimation MMSE processor, 520, 522, 524, 526 outputs b_(m),which is a vector of K symbol likelihoods or symbol bits for observationinterval m. Recall that K corresponds to the number of users andtherefore b_(m) represents the collection of symbol estimates for Kusers in observation interval m. Symbol bits are provided on the outputif thresholding is performed within the MMSE processor 520, 522, 524,526. The output from each MMSE processor, 520, 522, 524, 526, isintended to represent M K×1 vectors passed to the system area network(SAN) 530 which will then pass K M×1 frames of likelihoods (or symbols)to a bank of decoders 532, 534, 536, 538 for each user 1-K. The benefitof supplying likelihoods to the decoders 532, 534, 536, 538 is theimproved performance when using soft-decoders. It should be noted thatthe same processing nodes used for implementing the bank of MMSE filtersfor symbol estimation for processors 520, 522, 524, 526 can be used toimplement the K decoders, 532, 534, 536, 538 which are partitionedbetween the compute nodes. However, this embodiment also covers animplementation for hardware dedicated to MMSE processing and separatehardware for decoding.

The following discussion describes one embodiment for an implementationwhen a bank of processing nodes is used for both MMSE processing anddecoding. Symbol likelihoods are routed over the SAN 530 based on theassignment of decoders 532, 534, 536, 538 to processing components fordecoding. The SAN 530 routes the M K×1 vectors of symbol likelihoodsfrom the symbol estimation processing previously used to performMMSE-based symbol estimation on processors 520, 522, 524, 526 to thesame set of processing nodes that now perform the decoder processing onthe bank of decoders 532, 534, 536, 538. The SAN 530 passes K frames ofM×1 symbol estimates to the K decoders 532, 534, 536, 538, wherein theframes of symbols are represented by f_(k) for the K users. The frames,f_(k), represent a frame for each user that contains M symbols.

The number of decoders 532, 534, 536, 538 corresponds to the number ofusers, 1-K. The bank of decoders 532, 534, 536, 538 may implementhard-in, soft-out (HISO) or soft-in, soft-out (SISO) decoders. Inaddition to decoding, the bank of decoders 532, 534, 536, 538 performsdescrambling, de-interleaving, de-puncturing, and rate identification.The output of each decoder 532, 534, 536, 538 is, f_(k)′, which is a M×1vector representing an update of M likelihoods in the frame followingthe implementation of the decoders. In addition, the decoders 532, 534,536, 538 supply likelihoods of the information bits values d_(k)′ forthe same collection of symbols. These information bits are passed out asoutput 540, 542, 544, and 546. The number of bits per user is dependenton the data rate, and there bit likelihoods are available at any time inthe iterative scheme for post-processing such as a cyclic reductioncheck, which is used to determine frame errors. The number ofinformation bits transmitted per second is less than the number ofsymbols per second. Therefore, the number of information bits will beless than M, which defines the number of symbols per frame.

The updated likelihoods of the symbols K M×1 vectors of likelihoods,f_(k)′, are fed-back through SAN 550 and processed by a bank of MMSEfilters which are implemented on the M processor nodes, 560, 562, 564,566. The K M×1 vectors of likelihoods, f_(k)′, prior to the SAN 550 aresent to the MMSE filters as M K×1 vectors of likelihoods, b_(m)′.However, now the bank of MMSE filters is used for amplitude estimation575.

The same bank of 1-M symbol estimation processors 520, 522, 524, 526used to implement the initial MMSE filters for symbol estimation can beused for the further MMSE filter implementation for amplitudeestimation, illustrated by processing modules 560, 562, 564, 566. TheMMSE processing modules 560, 562, 564, 566 in amplitude updating focuson the same observation intervals analyzed during the symbol estimation.The MMSE processors 560, 562, 564, 566 at this stage implement thefunctionality of the filter module 415, 420, 430 of FIG. 4. This bank ofMMSE filters implemented by MMSE processors 560, 562, 564, 566 draw fromlocal memory 552, 554, 556, 558 to retrieve prior amplitude estimates,updated symbol estimates, S-matrices, and the received data vectorcorresponding to the observation interval m.

Referring back to FIG. 4, each amplitude processing module 415, 420, 430computes an amplitude update vector, Δa_(m), which is K×1 vectors. Eachamplitude update vector is passed to a central node that implements thefunctionality of the Amplitude Estimator 435. In FIG. 5, all individualupdate vectors from the amplitude estimation processors 560, 562, 564,566 are passed to a central processing node 575 via the SAN 570. Asdetailed herein, the amplitude estimate is updated by weighting the sumof all amplitude updating vectors, wherein the weighting is based onquadratic terms reflecting the uncertainty in the update, Q_(m). Thesequadratic terms are also passed to the central node 575 to provide thenecessary weighting.

The SAN 570 passes all Δa_(m) vectors (M vectors of size K×1) and Q_(m)matrices, (M hermitian K×K matrices with K(K+1)/2 unique elements) fromthe MMSE processors 560, 562, 564, 566 to the central processing node,575. The Amplitude Estimator 575 computes the functionality describedfor the Amplitude Estimator 435 of FIG. 4 and the updated estimate getsdistributed to all M processors used for the next MMSE-based symbolestimation. Therefore, Amplitude Estimator 575 passes the updatedamplitude estimate to SAN 580 that fan out these estimates for all Kusers to the M processors 520, 522, 524, 526 for the symbol estimationprocessing.

The updated amplitude estimates refreshes the amplitude estimates storedin the local memory 510, 512, 514, 516 coupled to the MMSE processors520, 522, 524, 526. Following the receipt of the updated amplitudeestimates, the next iteration of the MMSE-based symbol estimator isimplemented. Therefore, the MMSE modules 510, 512, 514, 516 perform thesymbol estimation processing and repeat the symbol estimation on thenext iteration using the new amplitude estimates. As described herein,the iterative processing continues until estimates of the bit error ratemetric achieves a certain level or until a fixed number of iterationshave been executed.

The hardware configuration presented in FIG. 5 is an attractiveimplementation because bandwidth requirements are minimized by passing atotal of only K·M floating point numbers over the SAN 530, except duringamplitude updating when the matrices Q_(m) are passed over the SAN 570to a central node 575. Bandwidth can be further minimized during theamplitude testing by approximating Q_(m) as a diagonal and thereforepass a total of 2K·M floating point numbers during amplitude updating.Since the MMSE operations, both symbol and amplitude, occur at the sameprocessing nodes on processors, 520, 522, 524, 526 and 552, 554, 556,558, less data is processed and efficient techniques can be used toexploit previous MMSE filter weights.

The communications configuration described in FIG. 5, is more easilyrecognized in FIG. 6, which shows 1-L nodes responsible for allprocessing. These L nodes pass data between themselves using a highspeed interconnect 625 for the system area network (SAN). The modules600 correspond to the time when the nodes 1-L are used to implement thebank of M MMSE filters for symbol estimation in M observation intervals.The symbol estimation processing at node 600 is comparable to processingperformed on MMSE processors 520, 522, 524, 526 shown in FIG. 5.

The number of L nodes depends upon the size of the problem, typicallythe number of K users, and the available hardware. For example, if K issmall, then more than one MMSE filter operation can be performed pernode in which case L<M. If K is large such that more than one node isrequired per MMSE filter operation, then L>M. The capability of the nodeis based on processing speed, local memory, design constraints, andconcept of operation.

The data from symbol estimation processing nodes 600 is passed by thehigh speed interconnect 625 to the decoder processing nodes 605. Thedecoder processing nodes 605 are L nodes used to implement a bank ofdecoders such as SISO decoders, wherein the decoders for K users aredistributed among the L nodes. This decoder processing corresponds tothe bank of decoders 532, 534, 536, 538 of FIG. 5. While FIG. 6illustrates an implementation using common hardware, for theimplementation where the hardware for MMSE and decoders are different,the L nodes 605 could refer to dedicated decoder hardware.

The communication between nodes from the MMSE symbol estimationprocessing stage to the nodes for the decoding stage of the algorithm isillustrated by the following example. For illustrative purposes, let thenumber of nodes equal the number of observation intervals, L=M. Thissimplification is not an architecture restriction, but simply aconvenient way to illustrate the communication between nodes. Based onthis simplification, the output of each node 600 in the symbolestimation section is a K×1 vector representing symbol estimates for theK users. Each node 605 in the decoder section is responsible fordecoding a subset of the number of users. To further simplify thediscussion, let each node 605 in the decoder section execute twodecoders. In other words, each node 605 performs the processing for twoprocessors of the bank of decoders 532, 534, 536, 538 from FIG. 5 sothat the number of nodes L in this example would be one half the numberof users K (L=½ K).

Therefore, based on these simplifications, for purposes of explanationonly, the symbols from the symbol estimation nodes 600 that correspondto users 1 and 2 in the first observation interval are two of the Ksymbol estimates determined in Node 1. The symbols from Node 1 of thesymbol estimation section 600 are transmitted to the Node 1 of thedecoder section nodes 605, wherein Node 1 of the decoders nodes 605represents the decoding of users 1 and 2 for the first time interval.The symbols for the users 1 and 2 in the second observation interval,Node 2, from the symbol estimations section 600 are passed over thehigh-speed interconnect 625 to Node 1 in the decoder section 605.Similarly, the symbols for the same two users, users 1 and 2, in thethird observation interval, Node 3, from the symbol estimation section600 are passed via 625 to Node 1 in the decoder section 605. Thisprocedure continues for the M observation intervals for the symbolscorresponding to the first 2 users, user 1 and user 2.

As should be readily apparent from this example, Node 2 of the decodersection 605 processes user 3 and user 4 for all the L observation nodes600; Node 3 of the decoder section 605 processes user 5 and user 6 forall the L observation nodes. By way of further explanation, Node 4 inthe decoder section 605 is responsible for decoding users 7 and 8, andreceives two symbols corresponding to users 7 and 8 from all the Lobservation nodes 600. For example, Node 1 of the symbol estimationsection 600 passes the symbol estimates of users 7 and 8 to Node 4 ofdecoder section 605. Similarly, Node 2 of the symbol estimation section600 passes the symbol estimates for users 7 and 8 for the 2ndobservation interval to Node 4 of decoder section 605. This procedurecontinues for all L nodes in the decoder section 605. Therefore, symbolestimates out of the MMSE symbol estimators 600 are routed from the Mobservation intervals to the K decoders of the decoder section 605 basedon the mapping of the K decoders to the L available processing nodes.

The next section is the amplitude estimation section 610 for nodes 1-L.The amplitude estimation section 610 represents L nodes implementing theMMSE stage of the amplitude estimator, as shown by modules 560, 562,564, 566 in FIG. 5. Prior to implementing amplitude estimation, theamplitude estimation nodes 610 receives all symbol likelihoods via thehigh speed interconnect 630 following the execution of the nodes fromthe decoders 605. As described in FIG. 5, the likelihoods of the symbolsout of the K decoders 532, 534, 536, 538 are routed back to M MMSEfilters 560, 562, 564, 566 for the amplitude estimation stage of thealgorithm. For this example, two decoders are implemented per node inthe decoder section 605 and one MMSE filter processor in the amplitudeestimation section 610 is executed per node. The output of Node 1 in thedecoder section is a M×2 array of likelihoods corresponding to the Msymbol likelihoods for the first two users after implementing thedecoder processing 605, descrambler, de-interleaver, and rateidentification. Node 1 in the amplitude estimation section 610 retainsthe symbol likelihoods for the first two users, user 1 and user 2, inthe first observation interval. Node 2 in the amplitude estimationsection 610 is responsible for the MMSE implementation over the secondobservation interval. Therefore, the second set of bits in the M×2 arrayis passed from Node 1 in the decoder section 605 to Node 2 in theamplitude estimation section 610. This is repeated for all M observationintervals. Therefore, the outputs of the decoder section 605 are passedto all nodes 610 computing the amplitude updating vector in the Mobservation intervals.

The next set of nodes is the amplitude weighing section 615 responsiblefor updating the amplitude estimate and which implements thefunctionality of the amplitude estimator 575 of FIG. 5. This processingis performed at a single node, and for illustrative purposes Node 3 isdepicted and described herein. It should be noted that the other nodesof this section 615 of the algorithm remain idle while the amplitudeestimation is completed. Therefore the high-speed interconnect 635passes the amplitude updating vector and quadratics from the L nodes inthe amplitude estimation section 610 Node 3 of the amplitude weighingsection 615.

The functionality of the Amplitude Estimator 435 of FIG. 4 provides amore detailed explanation concerning the weighing implementation. Theamplitude estimate calculated by Node 3 of the amplitude weighingsection 615 is “fanned-out” to all the L processing nodes for furtherprocessing of symbol estimation at the next iteration (I+1). Thus, thenew amplitude estimate for all K users is distributed via the high speedinterconnect 640 to all the L nodes of the next iteration symbolestimation section 620 for the I+1 iteration. The L nodes in the nextiteration symbol estimation section 620 re-compute the symbol estimates,using the new amplitude estimates, for the K users in the M observationintervals. The stages and data routing are repeated until the maximumnumber of iterations is achieved or the desired bit error rate isreached.

One aspect of the present invention is that it solves the previouslydescribed problem of tightly integrating parameter estimation, symbolhypothesis testing, decoding, and rate identification. The approach inone embodiment assumes a modulation scheme (e.g. CDMA) has beenutilized. The solution presented herein in one embodiment is awindowed-based Minimum Mean Squared Error (MMSE) approach toTurbo-decoding for joint signal demodulation, which is easily extendedto time-varying channels. The invention is based on an iterativedecoding solution that exploits error correction codes. In addition, theapproach of the present invention is suitable for symbol asynchronous aswell as symbol synchronous. Also, the algorithm used is suitable for avariety of signaling schemes such as M-ary Phase Shift Keying (MPSK).

It will be appreciated that implementation of the present inventionsolves the problem of the unknown channel parameters in state of the artdetectors. The present invention works with many varieties of multiuserdetectors and is easily implemented into different multiuser detectingsystems. Specifically, the MMSE symbol estimators in modules 600 and 620of FIG. 6 can be substituted with any of the known or referenced MUDalgorithms.

The present invention dramatically reduces the number of computationsneeded in a blind approach to joint signal demodulation and withoutpilot tones or training sequences, so that reliable operation can beachieved in a real-time implementation. It will also be appreciated thatsince the present technique does not require knowledge of trainingsequences or existence of a pilot signal, it is considered a blindapproach to symbol and data rate estimation. The efficientimplementation of blind estimation is realized because of the highdegree of parallelism. This provides an efficient implementation becauseof reduced number of operations associated with updating MMSE filter tapweights.

Further efficiency is realized because the number of iterations requiredto achieve a certain bit-error-rate for case of unknown amplitudes istypically similar to the case when the amplitudes are completely known.The present efficiency occurs because the degradation in bit-error-ratefor the case of unknown amplitudes is manageable relative to the case ofknown amplitudes. Thus, the present approach represents a new method toestimate channel amplitudes and extension to time-varying channels isobvious to those in the art. The present approach also represents a newmethod to reduce biases in amplitude estimates based on incoherentintegration.

The foregoing description of the embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed. Many modifications and variations are possible in light ofthis disclosure. It is intended that the scope of the invention belimited not by this detailed description, but rather by the claimsappended hereto.

1. A turbo-decoding system for joint signal demodulation, comprising: adata stream from a plurality of users divided into a plurality ofobservation intervals; a plurality of symbol processing nodes processingsaid observation intervals to compute symbol estimates for said datastream within said observation interval; a plurality of decoder nodesprocessing said symbol estimates and producing a plurality of symbollikelihoods; a plurality of amplitude update nodes processing saidsymbol likelihoods and calculating a plurality of amplitude updatevectors; and an amplitude estimator node processing said amplitudeupdate vectors and producing an amplitude estimate update, wherein saidamplitude estimate update is passed back to said processing nodes foriterative processing between said symbol processing nodes, said decodernodes, said amplitude update nodes, and said amplitude estimator nodeuntil a final condition is obtained.
 2. The system according to claim 1,further comprising a plurality of memory units, wherein at least onememory unit is coupled to each of said processing nodes and to each ofsaid amplitude update nodes.
 3. The system according to claim 1, furthercomprising a first system area network (SAN) coupling said processingnodes and said decoder nodes, a second system area network (SAN)coupling said decoder nodes and said amplitude update nodes, a thirdsystem area network (SAN) coupling said amplitude update nodes and saidamplitude estimator, and a fourth system area network (SAN) couplingsaid amplitude estimator and said processing nodes.
 4. The systemaccording to claim 1, wherein said final condition is selected from atleast one of the group consisting of: bit error rate metric level andfixed number of iterations.
 5. The system according to claim 1, whereinsaid processing nodes, said decoder nodes and said amplitude estimatornode are one set of nodes.
 6. The system according to claim 5, whereinsaid one set of nodes are each comprised of at least one processor. 7.The system according to claim 1, wherein said likelihood symbols fromsaid decoder nodes are accessible at any time for post-processing. 8.The system according to claim 1 wherein said processing nodes furthercomprises a thresholder to convert said symbol estimates to symbol bits.9. The system according to claim 1, wherein said decoder nodes performsde-interleaving, rate identification, de-puncturing and de-scrambling.